It is known that optimizing a deep learning model based on best validation loss achieves best quantitative results in image reconstruction, but resulting images are often blurry. In this study we propose an alternative way of optimization in which convolutional neural network (CNN) is trained beyond best validation loss to produce realistic MR images by monitoring Fréchet Inception Distance. The new approach generated sharper and more realistic images than the conventional optimization, providing a new insight into optimization for MR image reconstruction.
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